


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE     :					Tuple      =       logging.get_logger(__name__)  # pylint: disable=invalid-name

class     A__   (       snake_case__      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   ):
         super().__init__()
         self.register_modules(
             vae=__snake_case     ,   text_encoder=__snake_case     ,   tokenizer=__snake_case     ,   unet=__snake_case     ,   scheduler=__snake_case     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   )
    def 						a_     (			self     ,   __snake_case = "auto"       ):
         if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              snake_case											=							self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__snake_case       )
    def 						a_     (			self       ):
         self.enable_attention_slicing(__snake_case       )




    @torch.no_grad()
    def __call__(			self     ,   __snake_case     ,   __snake_case = 5_1_2     ,   __snake_case = 5_1_2     ,   __snake_case = 5_0     ,   __snake_case = 7.5     ,   __snake_case = None     ,   __snake_case = 1     ,   __snake_case = 0.0     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = "pil"     ,   __snake_case = True     ,   __snake_case = None     ,   __snake_case = 1     ,   __snake_case = None     ,   **__snake_case     ,   ):

         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							1
         elif isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							len(__snake_case       )
         else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__snake_case       )}'''       )

         if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''       )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(__snake_case     ,   __snake_case       ) or callback_steps <= 0)
         ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(__snake_case       )}.'''       )

         # get prompt text embeddings
         snake_case											=							self.tokenizer(
             __snake_case     ,   padding='''max_length'''     ,   max_length=self.tokenizer.model_max_length     ,   return_tensors='''pt'''     ,   )
         snake_case											=							text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              snake_case											=							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
              logger.warning(
                  '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''       )
              snake_case											=							text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
              snake_case											=							self.text_encoder(text_input_ids.to(self.device       )       )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         snake_case					,    snake_case					,    snake_case											=							text_embeddings.shape
         snake_case											=							text_embeddings.repeat(1     ,   __snake_case     ,   1       )
         snake_case											=							text_embeddings.view(bs_embed * num_images_per_prompt     ,   __snake_case     ,   -1       )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         snake_case											=							guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
              snake_case											=							42
              if negative_prompt is None:
                   snake_case											=							['''''']
              elif type(__snake_case       ) is not type(__snake_case       ):
                   raise TypeError(
                       F'''`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case       )} !='''
                       F''' {type(__snake_case       )}.'''       )
              elif isinstance(__snake_case     ,   __snake_case       ):
                   snake_case											=							[negative_prompt]
              elif batch_size != len(__snake_case       ):
                   raise ValueError(
                       F'''`negative_prompt`: {negative_prompt} has batch size {len(__snake_case       )}, but `prompt`:'''
                       F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                       ''' the batch size of `prompt`.'''       )
              else:
                   snake_case											=							negative_prompt

              snake_case											=							text_input_ids.shape[-1]
              snake_case											=							self.tokenizer(
                  __snake_case     ,   padding='''max_length'''     ,   max_length=__snake_case     ,   truncation=__snake_case     ,   return_tensors='''pt'''     ,   )
              snake_case											=							self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              snake_case											=							uncond_embeddings.shape[1]
              snake_case											=							uncond_embeddings.repeat(__snake_case     ,   __snake_case     ,   1       )
              snake_case											=							uncond_embeddings.view(batch_size * num_images_per_prompt     ,   __snake_case     ,   -1       )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              snake_case											=							torch.cat([uncond_embeddings, text_embeddings]       )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         snake_case											=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         snake_case											=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
         snake_case											=							text_embeddings.dtype
         if latents is None:
              if self.device.type == "mps":
                   # randn does not exist on mps
                   snake_case											=							torch.randn(
                       __snake_case     ,   generator=__snake_case     ,   device='''cpu'''     ,   dtype=__snake_case       ).to(self.device       )
                   snake_case											=							torch.randn(__snake_case     ,   generator=__snake_case     ,   device='''cpu'''     ,   dtype=__snake_case       ).to(
                       self.device       )
              else:
                   snake_case											=							torch.randn(
                       __snake_case     ,   generator=__snake_case     ,   device=self.device     ,   dtype=__snake_case       )
                   snake_case											=							torch.randn(__snake_case     ,   generator=__snake_case     ,   device=self.device     ,   dtype=__snake_case       )
         else:
              if latents_reference.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
              snake_case											=							latents_reference.to(self.device       )
              snake_case											=							latents.to(self.device       )

         # This is the key part of the pipeline where we
         # try to ensure that the generated images w/ the same seed
         # but different sizes actually result in similar images
         snake_case											=							(latents_shape[3] - latents_shape_reference[3]) // 2
         snake_case											=							(latents_shape[2] - latents_shape_reference[2]) // 2
         snake_case											=							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         snake_case											=							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         snake_case											=							0 if dx < 0 else dx
         snake_case											=							0 if dy < 0 else dy
         snake_case											=							max(-dx     ,   0       )
         snake_case											=							max(-dy     ,   0       )
         # import pdb
         # pdb.set_trace()
         snake_case											=							latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(__snake_case       )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         snake_case											=							self.scheduler.timesteps.to(self.device       )

         # scale the initial noise by the standard deviation required by the scheduler
         snake_case											=							latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         snake_case											=							'''eta''' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
         snake_case											=							{}
         if accepts_eta:
              snake_case											=							eta

         for i, t in enumerate(self.progress_bar(__snake_case       )       ):
              # expand the latents if we are doing classifier free guidance
              snake_case											=							torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
              snake_case											=							self.scheduler.scale_model_input(__snake_case     ,   __snake_case       )

              # predict the noise residual
              snake_case											=							self.unet(__snake_case     ,   __snake_case     ,   encoder_hidden_states=__snake_case       ).sample

              # perform guidance
              if do_classifier_free_guidance:
                   snake_case					,    snake_case											=							noise_pred.chunk(2       )
                   snake_case											=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              snake_case											=							self.scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                   callback(__snake_case     ,   __snake_case     ,   __snake_case       )

         snake_case											=							1 / 0.1_8215 * latents
         snake_case											=							self.vae.decode(__snake_case       ).sample

         snake_case											=							(image / 2 + 0.5).clamp(0     ,   1       )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         snake_case											=							image.cpu().permute(0     ,   2     ,   3     ,   1       ).float().numpy()

         if self.safety_checker is not None:
              snake_case											=							self.feature_extractor(self.numpy_to_pil(__snake_case       )     ,   return_tensors='''pt'''       ).to(
                  self.device       )
              snake_case					,    snake_case											=							self.safety_checker(
                  images=__snake_case     ,   clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
         else:
              snake_case											=							None

         if output_type == "pil":
              snake_case											=							self.numpy_to_pil(__snake_case       )

         if not return_dict:
              return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=__snake_case     ,   nsfw_content_detected=__snake_case       )








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




